In this paper we explore opportunities for learning in Multi-Agent Meeting Scheduling. We view this multi-agent task as fully distributed with several challenging characteristics: (i) agents have ownership of their own calendars; (ii) agents exchange information among each other with the goal of finding an open meeting time; (iii) agents can negotiate multiple meetings concurrently. We have implemented a negotiation strategy in which the agents communicate all their available time slots. This "open negotiator" is designed to reflect the open calendar approach of Microsoft Outlook in a distributed setup. We show where this negotiation strategy fails to lead to efficient scheduling and discuss how agents could use learned information to improve social welfare and scheduling performance.